利用机载LiDAR点云数据进行建筑物重建是当今摄影测量与遥感领域的一个热点问题,特别是复杂形状建筑物模型的精确自动构建一直是一个难题。本文提出一种基于关键点检测的复杂建筑物模型自动重建方法,采用RANSAC法与距离法相结合的分割方法自动提取建筑物屋顶各个平面的点云,并利用Alpha Shape算法提取出各个平面的精确轮廓,根据屋顶平面之间的空间拓扑关系分析建筑物的公共交线特征,在此特征约束下对提取的初始关键点进行修正,最终重建出精确的建筑物3维模型。选取不同类型复杂建筑物与包含复杂建筑物的城市区域点云进行实验,结果表明该算法具有较强实用价值。

Currently, automated reconstruction of buildings from data acquired by airborne LiDAR has been an important research topic in photogrammetry, especially for the precise reconstruction of complex buildings. Given the various and complex structures of buildings and the discrete distribution of laser data, automatically reconstructing complex buildings using airborne LiDAR data is complex. Certain methods are only suitable for buildings with simple structure, whereas some methods reconstruct the building models with the aid of other data source, such as remote sensing images and ground plans. Hence, this study proposed an automated approach for complex shape building reconstruction based on key point detection that only makes use of airborne LiDAR point cloud to solve the problems on the automated reconstruction of complex buildings.The point clouds of different roof planes are extracted automatically by combining RANSAC segmentation and space segmentation. For each plane, the exact contour is picked up using the Alpha Shape algorithm. Public intersection line features are determined by topological relation of these planes, which will help correcting the initial key points. Finally, the precise building model is obtained. Detection of the key points is important for our method, which contains extraction of initial key points, judgment of topological relation of planes, correction of key points, and model reconstruction. During judgment of topological relation of planes, three pubic intersection line features should be determined, namely, public line segment, public radial, and public line. The final key points are then calculated correctly according to these restraints.Two different types of buildings and one urban area were chosen to verify our method. Building 1 is composed of many horizontal roofs with different height levels, without intersection among those roof planes; whereas Building 2 consists of some gable roofs with relative more complex relationship. The experimental results showed that RANSAC and space combined segmentation is fantastic, the initial key points present the sketchy structure of the building roof, the vectorized roof models show the public intersection line features reasonably, and the final reconstructed building models fit the remote sensing images well. To further validate the effectiveness of our approach, the same two buildings were processed using TerraSolid and the models were compared with our results. The comparison proved that the accuracy of our approach is close to that TerraSolid. The selected urban area contains flat roof buildings, gable roof buildings, L shape buildings, hipped buildings, and complex structure buildings combined with the above typical shapes. Successful reconstruction of this area proves that our approach is suitable to urban area point cloud with complex shape buildings and is effective and practicable.Our approach realized the automated reconstruction of complex shape building models by point cloud segmentation, contour extraction, and key point detection. Experimental results of different types of buildings and one urban area showed that:(1)Our method can reconstruct complex-shaped buildings effectively;(2)Compared with TerraSolid the reconstructed results, the accuracy of our method is close to that of TerraSolid and is valuable;(3)The method is restricted by point cloud segmentation. If the threshold in the segmentation is rigorous, some roof plane point clouds would be lost, which would have side effect on the final reconstruction results.